In light of this, we argue that few-shot segmentation task can be reformulated as semantic correspondence that aims to find pixel-level correspondences across semantically similar images, which poses some challenges from large intra-class appearance and geometric variations [1]}, [2]}, [3]}. Recent approaches for this task [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}, [12]} carefully designed their models analogously to the classical matching pipeline [13]}, [14]}, i.e., feature extraction, cost aggregation and flow estimation. Especially, the latest works focused on cost aggregation stage [7]}, [8]}, [17]}, [18]}, [10]}, [20]}, [12]}, [22]} and showed outstanding performance.
